Chief Data Scientist or business analyst intern?

Data Science predicts behaviour, Business Intelligence provides data-driven insights for operational improvement, and Data Management creates and manages the infrastructure to do both.

Despite the hype, the need for in-house data science is actually quite rare. Proprietary AI & ML is only required when a large part of investor value is driven by predictive analytics. In the majority of cases, linear algorithms perform better and cheaper, and when AI is a necessity, third-party algorithms can often be more cost-effective and have little or no impact on valuation at exit.

Since the rise of SaaS and digital-first consumption, in both B2C and B2B markets, the creation and systematic use of business intelligence has become essential: as markets become transient and users become more complex, the ability to mine and exploit exponentially growing volumes of data has ceased to be a competitive advantage – it is now a requirement for company survival, and quite possibly investor success.

User-friendly dashboards are now the visible side of algorithms and data mining, but these rely on the highly technical skills of data management – a hidden world of data architecture, infrastructure design, distributed storage and programmatic manipulation.